Job Description
Job description
Key Responsibilities
• Design and own end-to-end AI system architecture using MCP (Model Context Protocol)
• Lead AI integration across products (LLMs, agents, tools, APIs, data pipelines)
• Architect scalable, secure, and low-latency AI-first platforms
• Define agent orchestration, tool calling, memory, and context strategies
• Collaborate with product, backend, frontend, and DevOps teams
• Review code, enforce best practices, and mentor AI engineers
• Drive LLM model selection, fine-tuning, evaluation, and deployment strategy
• Ensure observability, cost optimization, and governance of AI systems
🔹 Required Skills
Architecture & Systems
• Strong experience as AI Architect / Tech Lead / Principal Engineer
• Deep understanding of MCP architecture (context servers, tools, resources)
• Distributed systems, microservices, event-driven design
AI & LLMs
• Hands-on with OpenAI / Anthropic / Gemini / open-source LLMs
• Agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, etc.)
• RAG pipelines, embeddings, vector databases (Pinecone, Weaviate, FAISS, etc.)
• Prompt engineering, tool calling, function schemas
Engineering
• Python (must), plus Node.js / Java / Go (any)
• REST, GraphQL, streaming (WebSockets)
• Cloud : AWS / GCP / Azure
• Docker, Kubernetes, CI / CD
🔹 Nice to Have
• Production MCP server implementations
• AI security, compliance, and guardrails
• Experience building AI platforms / copilots / enterprise AI
• Startup or high-scale SaaS experience
